使用椭圆模板匹配和粒子群优化算法检测血管内超声（IVUS）图像中的动脉粥样硬化斑块轮廓
Contour detection of atherosclerotic plaques in IVUS images using ellipse template matching and particle swarm optimization.
作者信息
Zhang Qi, Wang Yuanyuan, Ma Jianying, Shi Jun
机构信息
School of Communication and Information Engineering, Shanghai University, China.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5174-7. doi: 10.1109/IEMBS.2011.6091281.
It is valuable for diagnosis of atherosclerosis to detect lumen and media-adventitia contours in intravascular ultrasound (IVUS) images of atherosclerotic plaques. In this paper, a method for contour detection of plaques is proposed utilizing the prior knowledge of elliptic geometry of plaques. Contours are initialized as ellipses by using ellipse template matching, where a matching function is maximized by particle swarm optimization. Then the contours are refined by boundary vector field snakes. The method was evaluated via 88 in vivo images from 21 patients. It outperformed a state-of-the-art method by 3.8 pixels and 4.8% in terms of the mean distance error and relative mean distance error, respectively.
在动脉粥样硬化斑块的血管内超声（IVUS）图像中检测管腔和中膜-外膜轮廓对于动脉粥样硬化的诊断具有重要价值。本文提出了一种利用斑块椭圆几何先验知识的斑块轮廓检测方法。通过椭圆模板匹配将轮廓初始化为椭圆，其中匹配函数通过粒子群优化最大化。然后通过边界向量场蛇形算法对轮廓进行细化。该方法通过来自21名患者的88幅体内图像进行评估。在平均距离误差和相对平均距离误差方面，分别比一种先进方法高出3.8像素和4.8%。
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